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Sliding mode control method based on support vector machine studies

Author: LiuMingDan
Tutor: ChenZhiMei
School: Taiyuan University of Science and Technology
Course: Control Theory and Control Engineering
Keywords: Sliding Mode Control Global sliding mode control Support Vector Machine Particle Swarm Optimization
CLC: TP13
Type: Master's thesis
Year: 2011
Downloads: 52
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Abstract


Discrete-time control systems , studied three different sliding mode control method based on support vector machine . The support vector machine online to adjust the system parameters to determine the uncertainties in the sliding mode control supremum buffeting weaken the sliding mode control high frequency system , improve the quality control of the system . Finally, simulation results show that the correctness and validity of the method . First, a sliding mode control based on support vector machines and particle swarm optimization method . Strong generalization capability of support vector machine online Reaching legal parameters to adjust , to overcome the pre-set limit of reaching law parameters required in the conventional sliding mode control , improve system quality control . Introduction of particle swarm algorithm to solve the shortcomings of computationally intensive support vector machine , to speed up response time , weaken the system buffeting . Then , a global sliding mode control method based on support vector machine . Approaching the segment does not have the sliding mode characteristics , the design of the entire sliding mode controller using particle swarm optimization and support vector machine global sliding mode line adjustment factor and the sliding mode reaching law sliding mode control has strong robustness , the parameters in the elimination of reaching segments of the the system sliding mode control , so the the system trajectory outset into the sliding surface , and to overcome the unknown parameter perturbation and external interference , the system throughout the course of the campaign has strong robustness , improve the control performance of the system . Finally, for a class of uncertain discrete control systems , the introduction of the upper bound of adaptive learning based on support vector machine . The method will separate the uncertainties in the system , to form joint upper bound of the uncertain amount , using support vector machine to measure the difficult problem of the unknown upper bound of self- learning , to resolve uncertainties in the practical application of the upper bound , the method can reduce the conventional sliding mode control conditions , while maintaining strong robustness of the sliding mode control system , weaken the system buffeting .

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